External data for background.
Using an external background dataset is an alternative way to synthesize background region although an image without background is not always accessible. In this experiment, we use DTD [1]} (texture image dataset). The background region is replaced with a randomly cropped patch from DTD dataset, and a model is trained in the same way as LDET. See the appendix for more details of training. The resulting AP and AR are 11.1 (\(\text{LDET}+0.9\) ) and 32.6 (\(\text{LDET}-2.2\) ) respectively in bounding box localization. The dataset includes a considerable number of objects despite being primarily a texture dataset, which is probably the cause of degradation in AR.
Our data augmentation method is comparable or better than the use of external background images.
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